Kediska Injineerinka Feature

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. What work does feature engineering involve before model training?
   • Doorashada GPU-ga ugu dhakhsaha badan ee tababarka
   • Qorista shaqada khasaaraha ee moodeelka
   • U beddelashada xogta cayriinka ah una beddelo agabka wargelineed ee moodalku wax ka baran karo
   • Gelinaya nooc wax soo saarka
2. Why should a proposed engineered feature be evaluated with the chosen model and task?
   • Every extra feature guarantees higher accuracy
   • Its usefulness depends on what it represents and how the model uses it
   • Input representation never affects predictions
   • Changing features always matters more than every algorithm choice
3. A feature uses an outcome recorded only after the prediction is needed. Which feature-engineering problem does this illustrate?
   • A necessary improvement in causal identification
   • A harmless change to a file extension
   • A requirement to use more graphics hardware
   • Prediction-time information leakage
4. When fitting a standardization step for a train-and-held-out evaluation, which data should supply its mean and scale?
   • The training data, with those fitted statistics reused on held-out inputs
   • The test data only
   • All data after examining final test outcomes
   • A different refit on every evaluation example
5. Midkee kuwan ka mid ah farsamada injineernimada sifo caadi ah ee xogta qaybsan?
   • Kordhinta heerka waxbarashada
   • Hal-kulan ama bartilmaameedka codaynta
   • Ku darida GPU-yo badan
   • Isku shaandhaynta magacyada faylka
6. With OneHotEncoder configured as handle_unknown ignore and no dropped category, how is an unseen category represented for its feature?
   • An extra indicator column created automatically for that new category
   • An immediate error regardless of the stated ignore setting
   • All zeros in the indicator columns for that feature
   • The outcome target value copied into the indicator columns
7. Why can extreme numeric observations affect a StandardScaler transformation?
   • Its estimated mean and variance are sensitive to outliers
   • It uses medians and interquartile ranges instead of means and variances
   • It ranks observations without using their numeric magnitudes
   • It clips every value to fitted quantiles before estimating scale
8. In a stated degree-two interaction feature x1 times x2, what is its value for x1 three and x2 four?
   • Toddoba
   • Mid
   • Twenty-five
   • Laba iyo toban